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In the context of automated testing in the automotive industry, the accurate and effective detection of target icons on vehicle-machine interfaces is crucial for ensuring the efficiency and precision of automated testing processes. This paper addresses the challenge of effectively detecting vehicle-machine interface icons by proposing a three-stage verification detection algorithm based on machine vision. This paper employs an enhanced template matching technique to achieve precise positioning and initial verification of car and machine icons. Subsequently, a two-stage verification process is implemented, which focuses on detecting local changes using pixel blocks. The three-stage verification is accomplished through a three-channel separation detection of characteristic pixel points based on RGB values. Additionally, this paper utilizes the collaborative capabilities of LabVIEW and OpenCV to overcome integration challenges. This joint programming approach bridges the gap between LabVIEW and OpenCV. Experimental results unequivocally underscore the paramount significance of the three-stage verification detection algorithm, rooted in the realm of machine vision. The algorithm&#x2019;s novel three-stage approach, intricately designed to combat background interference, not only elevates the precision of icon detection but also introduces an ingenious mechanism for classifying detection results. In this regard, it successfully overcomes the limitations that have historically hindered traditional machine vision algorithms, ultimately demonstrating its paramount importance in the field of computer vision.

Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing through a Three-Stage Machine Vision Verification Algorithm